Evaluation of comprehensive early warning for higher education institutions’ cloud model of simulated enterprise management cockpit

The cross-disciplinary virtually simulated platform for enterprise management in universities’ new business courses is an initiative practical framework based on scenario-driven tasks. However, there is a prominent conflict between the rapid operating cycle of simulation enterprises plus their fierce competitions and the strategic demand for real-time analysis for operational data. Based on such demand, this study takes the development method of the simulated enterprise management cockpit from Guangzhou Huashang College as an example. It adopts the combined weighting method based on cloud models to determine indicator weights, then qualitative and quantitative data analyses are conducted from five aspects: “business, finance and operation”, “customer management and marketing”, “internal operational objectives”, “product development strategy”, along with “team building and management”. This approach achieves a comprehensive evaluation and early warning of the enterprise management process. Specifically, the subjective weights are determined by the Analytic Hierarchy Process, while the objective weights by the entropy weight method, finally verified by cloud model evaluation of its overall indicator performance. The design can evaluate the comprehensive performance of enterprise management indicators and students’ activity participation through the cloud-based application and the digital cockpit, so as to fully presents the enterprise’s overall management level, along with judgement of whether it is reasonable through pointers in different colors. In addition, apparent indicator-related characteristics are also utilized to assess future decision-making directions. Finally, this comprehensive approach can timely optimize operation strategies and facilitate budget allocation for future development.


Background
In response to fierce competitions in current business environment, enterprises surely need swift and precise data collection and analysis for decision-making and performance monitoring.In this context, the enterprise management cockpit as an integrated information platform has developed into a powerful tool for the management to better understand current enterprise status and trends.This study centers around the pivotal role of such cockpit in promoting efficient management practices through discussions of its design and implementation, together with relevant advice accordingly.
The National Education Work Conference in 2023 proposed to fully implement the digital education through national strategies, so as to inject new vitality to educational development [1].More specifically, major tasks in 2023 for the Ministry of Education's Higher Education Department can be summarized as "further promotion of 'Four News' (new majors, institutions, models, and paradigms)" along with a new step forward in independent talent cultivation.Furthermore, disciplinary integration is highlighted, featured by the development of new liberal arts, quality and organizational model innovations [2].
To this end, followed by the idea of new business, business schools with managementrelated disciplines have embraced the new education paradigm to cultivate talents in comprehensive cross-disciplinary enterprise simulation courses, thus addressing the inadequate support for higher operational efficiency of simulated enterprises.Traditionally, such issue is primarily manifested by the conflict between the complexity and urgency of the operational process and the guidance for value enhancement in simulated enterprises, along with the mismatch between the training for decision-makers and their actual performance.In addition, the simulation process highly needs a cloud-based evaluation and analysis tool that coordinate with overall enterprise operation.This tool can facilitate real-time integration of and insight into operation data and team-building evaluations.In addition, early warning through visible meters are available for decision-makers to rapidly assess their own capabilities, then realize efficient decisions and agile management.
Correspondingly, the simulated enterprise management cockpit from Guangzhou Huashang College is adopted in this study.By studying the indicators' design system for the cloudbased simulated intern enterprise management cockpit, a combined weighting method based on cloud models can be utilized to integrate qualitative and quantitative data evaluation.Specifically, real-time quantitative data comes from databases, while timely qualitative data from robot collection of the enterprise's operational processes and evaluation results of team-building activities.Then, the weighting algorithm is employed to evaluate relevant data by different levels and corresponding results are presented visually.Through timely diagnosis and early warning provided on the cloud platform, decision-makers can optimize their management, as shown in Figs 1 and 2.
In summary, considering current trends in information technology and management disciplines, it is of practical significance to deal with the building of the early warning evaluation system for the simulated enterprise management cockpit in higher education institutions from the perspective of cloud model and combined weighting, furthermore in promoting the visual methods of enterprise management empowered by data intelligence.To be noted, the simulated enterprise management cockpit from higher education institutions will be hereinafter referred to as "the enterprise management cockpit", and Abbreviated as "EMC".
The importance of this research of simulated enterprise management cockpit in this paper: (1) constructing a comprehensive operational safety evaluation index system, and comprehensively and scientifically evaluating the operating status and development trends of the enterprise through the combination weight method; (2) Through advanced data visualization and information technology, the core business and financial indicators of the enterprise are presented intuitively, enabling managers to respond quickly and combining macro and micro indicators to timely identify potential risks and achieve accurate decision-making; (3) By applying the principles of cloud models, it is possible to provide real-time feedback on the warning levels of enterprises under different operating conditions, provide targeted warning information, and help prevent and respond to potential business risks in a timely manner.
1.2 Research meaning of the early warning evaluation system for the EMC 1.2.1 Theoretical implications.This study aims to address the shortcomings of talent cultivation methods in economic management disciplines, align with platform based big data technology, practice new business education concepts, innovate cross disciplinary enterprise simulation comprehensive practice talent cultivation methods, and break through the bottleneck of traditional platform based talent cultivation methods in supporting the operational effectiveness of simulation enterprises.On the one hand, the theoretical significance of this study lies in solving the problems of structuring, systematizing, and scientificity of enterprise decision-making information.It can be an evaluation and analysis tool that fits the panoramic view of enterprise operation, and can integrate and perspective enterprise operation process data and team building activity evaluation results in real time; On the other hand, the theoretical significance of this study lies in whether the corresponding information of decision-making positions, such as CEOs, CFOs, and CMOs, matches the actual functional data needs of the positions; Finally, the theoretical significance of this study lies in its ability to provide instrument based warning support, distinguishing warning levels through color or indicator values, assisting enterprise decision-makers in quickly measuring their own capabilities, efficiently predicting decisions, and achieving agile management.

Practical implications.
Apart from the object in this study, investigations and exchanges have been conducted with similar simulation platform courses at Guangdong University of Finance and Guangdong Baiyun University.According to feedbacks, in the building of cross-disciplinary and comprehensive intern simulation platforms in higher education institutions, cloud management mobile services should be integrated.Moreover, a scientific and reliable evaluation framework for management cockpit system should be fully utilized to support informatization decisions.Finally, both physical wall-mounted panel displays and synchronized queries on mobile terminals are available to enterprises' digital management of strategic performance.
Therefore, relying on the current application status of the simulation platform at the School of Business of Guangdong University of Finance, there is an urgent need for objective alignment of talent cultivation, together with collective and timely feedback on simulation effects.So that intern traces and competency levels of respective majors and positions are visually clearer, further highlighting the significance of applying this approach in teaching management of business majors in the modern era.

Research methodology
1.3.1 Literature research method.Firstly, it is necessary to collect research, theoretical and practical significance, as well as domestic and foreign research reviews on decision support systems and information system evaluation.By summarizing and summarizing previous research results, the research ideas and methods of this article can be obtained.Based on the reference of previous research results, further research can be conducted.
1.3.2Case study method.The case analysis method requires a full understanding of the actual situation of the case enterprise.Only by comprehensively understanding the situation of the case enterprise can the conclusions drawn have credibility and persuasiveness, and reach conclusions with high credibility.This conclusion will be universal, deepen the understanding and interpretation of similar events, and provide a certain reference for the development path of similar enterprises.
Through case analysis and research on the design and application process of the management cockpit system at Guangdong University of Finance and Economics Huashang School, the key factors affecting the success of the project and the focus of subsequent optimization are summarized.
1.3.3Inductive summary method.Through the digital and intelligent reform of efficient talent cultivation, the big data teaching concept and teaching methods of economic and management simulation platforms, and the collection of data related to enterprise management cockpit, enterprise performance management, financial analysis, and other aspects in recent years, more objective theoretical and practical application support are selected.

Literature review of international researches
In terms of modern enterprise management, the theory of Business Intelligence (BI) has developed from data warehouse to Balanced Scorecard (BSC), further evolving into the panel-style management tool known as "management cockpits" supported by visual virtual information technology.Through this approach, an integrated strategic performance system can be built for a visual and comprehensive management application that covers the whole picture and details, thus supporting all decision-making by multidimensional indicator dashboards as follows: First, the current status of international BI researches.The preliminary BI theory, including data warehouse, online analytical processing and data mining, is rooted in transforming existing data within enterprises into knowledge, thereby contributing to informed business operational decisions [3].
The next step forward primarily in the 1990s mainly developed in databases and data modeling, with the new concept of directed graph workflow mining models-data mining [4].
More recent advancements in this aspect refer to the theory of intuitive visualized management, namely the dashboard management theory.Thereby, relevant intelligent analytical concepts are more clear, including visual dashboard analysis, control and self-service analysis, integrated data product chains, cloud-based management data analysis, mobile terminal analysis and IoT data analysis [5].
Moreover, the rise of service-oriented personal cloud computing requires new technologies for asset discovery, exchange, and management.In response, a large-scale distributed system on unified personal clouds is then proposed, namely interactive cockpits that manage personal clouds and their alliances.In this way, there is less reliance on central infrastructure, while greater balance between service supply and consumption [6].
To deal with cross-functional communication and collaborative management within the management cockpit, a control and management process is established based on intellectual property and strategic orientation.This approach has been proved successful within enterprises and interdisciplinary management teams, enabling structural, categorized, and managing functionsv-with abundant visualized information along with extensive analysis and simulation options for comprehensive and sustainable performance management [7].
In terms of BSC's application in higher education, the recommended method is to formulate strategies for implementing BSC within educational institutions, then apply and observe its effectiveness in organizational assessments.The aim is to define a management and tracking system for industry bench-marking [8].
During the strategic implementation phase, the management proposes to interpret strategic execution as a dynamic decision-making task.Learning from previous experience of solving complex problems and performance measurement, more explorations are conducted on whether factors such as BSC cockpit, intelligence, and knowledge can explain variations in strategic execution performance [9].
When considering differentiated decisions of implementing strategies and outcomes resulting from various cockpits, scholars proposed to integrate ideas related to strategic operations.Therefore, they design a closed-loop control task for strategic implementation and advocate a BSC strategy map cockpit.This cockpit provides participants with a concentrated and useful interface with both theoretical and practical contributions during implementation, also facilitating the modeling considering the impact of management performance measurements [10].
Second, The current international research status of evaluating enterprise management information systems.So far, popular evaluation methods for information systems worldwide include qualitative expert analysis using the Delphi method, fuzzy composite evaluation based on fuzzy matrices, the quantification of qualitative data through a hierarchical quantitative analysis using the Analytic Hierarchy Process, multi-criteria decision analysis, along with expectation models that emphasize customer satisfaction.Specifically, ideas on strategic performance evaluation are as follows: In terms of performance management, relevant theories proposed to integrate an enterprise's overall strategy with its financial indicators and non-financial information.To be noted, the crucial connection between overall strategy and performance indicators during evaluation part is fully highlighted, therefore a Performance Pyramid model is designed accordingly [11].
In addition, the reform-centered view of performance evaluation reveals enterprises' sustained development capability, which is theoretically sophisticated, but rarely applied in practice [12].
The financial management system EVA (Economic Value Added) came out, which combines decision-making, incentive, and performance evaluation.In particular, it emphasizes enterprises' costs of all capital inputs and their long-term development [13].
Serving as a performance assessment system, the BSC is of great significance in implementing enterprise strategies.It provides a framework that focuses on key management processes, assessing an enterprise's past performance, together with its future development potential and competitiveness [14].
When evaluating enterprise performance, the "Four-Dimension" framework proposed by Robert Hall is adopted, including quality, lead time, resource utilization, and human resource development.These four non-financial indicators are included into the performance evaluation system and improvements in any of these dimensions should not sacrifice others, so as to reduce competitive risks.
Regarding decision support systems, the solution is to integrate visualize method and decision modeling capabilities with human insights and interaction abilities, thus developing a decision support system known as Forest Community-DSS.By leveraging techniques like DT (Decision Theater), FC-DSS and relevant technologies can enhance stakeholder involvement in decision-making through higher participation frequency, more input from stakeholders, support given to the development and evaluation of alternative solutions, and optimized selection of preferential alternatives, as highlighted by Boukherroub, D'amours, & Ro ¨nnqvist [15].
Third, Overseas research status of management cockpit systems.Among developed countries, the management cockpit system serves as sophisticated decision support within information systems, which demonstrates an advanced development stage of business intelligence systems that evaluated by the fourth-generation BSC concept.This system has now been widely adopted in various industries, with verified values and efficiency in enterprise competition and operations.So far, prominent global suppliers of such cockpit information system mainly refer to SAP, Oracle, Microsoft, SAS, Qlik, Tableau and Microstrategy, basically monopolies in sectors such as telecommunications, finance, retail, and insurance.

Literature review of research status in China
In China, domestic researches on evaluating enterprise performance primarily focuses on local applications of advanced international models.Apart from the characteristics of Chinese enterprise s' supply chains, scholars adopted a hierarchical approach based on BSC theory.They emphasize the need for comprehensive evaluations considering multiple factors, thus upgrading the indicator system that integrates BI theory.
First, the domestic BI research status.The initial BI research in China dates back to a relevant report in 1998, presenting several views discovered in the management community.After that, theoretical researches in specific areas emerged, such as starting from interpreting BI concept from management, technology, and application perspectives [16], then corresponding implementation strategies [17], and further control recommendations during BI implementation [18].
The cloud technology and business intelligence was integrated, featured by resource sharing, low costs, and real-time interactions [19].In addition, a system dynamics model for strategic performance evaluation was introduced, which effectively combines relevant theories and research methods of BSC and system dynamics.In detail, this model had an extra BSC dimension of social responsibility, finally building a dynamic five-dimensional model with an indicator system and strategic map for evaluating enterprise strategic performance by simulation software [20].
To upgrade agile enterprise management, a management cockpit was proposed to integrate various business units and break down the barriers between departments.This efficient method functions well in handling with huge amounts of isolated cross-organizational business data at different management levels, thus mitigating "information silos" and enhancing the enterprise's competitive prowess in dynamic markets [21].What's more, a multi-dimensional intricate system of budget analysis indicators was developed to support lean management and facilitate feedback.Particularly, the management cockpit works as a budget compass and baton, directing towards efficient resource allocation and continuous improvement [22].
Second, the domestic research status of information system evaluation.Despite being not mature, relevant domestic researches on information system evaluation have already yielded significant results.One previous study proposed an evaluation system for enterprise informatization, featured by 20 primary indicators and specific calculation methods.This study was also a pioneer in measuring the effectiveness of enterprise informatization by quantifying the correlation between the level of informatization and enterprise performance [23].
After several years of technology advances, a comprehensive life-cycle evaluation system was put forward, which utilized the hierarchical grey method and verified its scientific validity and applicability through case study [24].
In order to further upgrade the evaluation system, user-friendly idea prevailed, specifically valuation metrics of relevant users in different stages were established, including researches on the evaluation indicator system for building enterprise information system considering both enterprise management personnel and front-line operators [25].
Based on the multi-dimensional utility theory, scholars introduced a flexible information system evaluation model accordingly in multi-objective decision-making.Supported by a comprehensive and objective indicator system, a hierarchical structure for information system evaluation utilizing a multi-dimensional utility model, along with specific merging rules and weights, the overall effectiveness of information systems has been fully validated [26].
Among various relevant evaluation methods, scholars suggest a combination of two or more methods [27].
Then, scholars proposed an enhanced value evaluation model according to stakeholder theory.This model combines both financial and non-financial indicators in three dynamic dimensions that reflects the organization's value and strategic performance, namely growth performance, profitability performance, and risk performance [28].
From the perspective of cultivating students' innovation and entrepreneurship capabilities, the dashboard can be utilized, so that a pragmatic virtual simulation teaching platform can visualize relevant resources based on business management disciplines.In this way, traditional low efficiency in transferring theoretical knowledge into practical skills is no longer a problem.Instead, this method can motivate an active entrepreneurial mindset and participation, so as to enhance the entrepreneurial qualities of high-end management professionals [29].
Furthermore, through the above literature review research, the performance of business functions in evolving into intelligent enterprise management cockpit management services lies in: (1) the ability to utilize technologies such as big data, cloud computing, artificial intelligence, and automation to integrate ERP databases with other systems; (2) Can provide agile, scenario based, and intelligent management and decision-making services for enterprises, help them improve their digital capabilities, and achieve maximum data value; (3) It has the characteristics of low code, light configuration, easy operation, strong support, open integration, compatibility with multiple platforms, and emphasizes data security and privacy protection.In summary, this enterprise management service can provide rich and multi-dimensional custom data display, report visualization, and interaction capabilities, helping enterprises better manage and analyze data, making wiser decisions, and emphasizing data security and privacy protection.

Enterprise management cockpit
The enterprise management cockpit is a management information center system that provides "One Stop" decision support for senior management.It visually displays the key indicators (KPIs) of enterprise operation in the form of a cockpit, through various common charts (speedometer, volume bar, warning radar, and radar), intuitively monitors the operation of the enterprise, and can provide early warning and mining analysis of abnormal key indicators.
When enterprise managers have no time to query in the physical cabin, they can synchronously query the corresponding dial data with mobile phones and other mobile terminals under Internet conditions, so as to achieve an integrated online and offline application.
The management cockpit incorporates the management philosophy of the balanced scorecard, which unfolds the company's vision and strategy through a strategic map layer by layer into a set of dynamic and measurable KPI indicators.The internal processes and team learning aspects emphasized by the balanced scorecard correspond to the blue wall content of the management cockpit, the business, finance, and operation aspects correspond to the black wall content of the management cockpit, and the product development and customer aspects correspond to the red wall content of the management cockpit;

Analytic Hierarchy Process
Analytic Hierarchy Process (AHP) is a qualitative and quantitative decision-making method proposed by Professor Sati in the early 1970s in the United States.Its characteristic is to conduct in-depth analysis of the essence, influencing factors, and internal relationships of complex decision-making problems, and on this basis, use quantitative information to mathematize the thinking process of decision-making, solve complex decision-making problems with multi-objective, multi criteria, or unstructured characteristics, thereby making decision-making methods more convenient.This method decomposes into different hierarchical structures in the order of the overall goal, sub goals at each level, evaluation criteria, and specific backup plans.Then, the method of solving the judgment matrix eigenvectors is used to obtain the priority weights of each element in each level for a certain element in the previous level.Finally, the method of weighted sum is used to gradually merge the final weights of each backup plan for the overall goal.The one with the highest final weight is the optimal plan.
In modern decision-making assistance applications, with the help of information technology, the elements related to decision-making are decomposed into levels such as goals, criteria, and plans through matching.The network system theory and multi-objective comprehensive evaluation method are applied to propose a hierarchical weight decision analysis method.
The management simulation platform of the Huashang School of Guangdong University of Finance and Economics is facing a panoramic simulation of socialized operations.In the face of centralized data at various levels, it is necessary to determine the impact weights and select the best ones, in order to select key indicator data that can affect operations.

Fuzzy comprehensive evaluation method
This comprehensive evaluation method transforms qualitative evaluation into quantitative evaluation based on the membership theory of fuzzy mathematics.The basic principle is: first, determine the set of factors (indicators) of the evaluated object and evaluate the set of levels; Secondly, determine the weights and membership degree vectors of each factor separately to obtain a fuzzy evaluation matrix; Thirdly, perform fuzzy operations on the fuzzy evaluation matrix and the weight vectors of factors, and normalize them to obtain the fuzzy comprehensive evaluation results.
The management simulation platform of Huashang School of Guangdong University of Finance and Economics requires enterprises to objectively reflect the actual situation of their business processes, including the assessment of their own human capital, timely feedback on their business operations, and business dealings with peripheral institutions such as industry and commerce, taxation, and auditing.Both quantitative and qualitative data are needed, and they need to be unified into a quantitative caliber.Therefore, modern technology and fuzzy mathematics are needed to integrate them together.

Cloud model theory
Cloud model theory is a combination evaluation method that combines qualitative knowledge with quantitative data to integrate uncertain data.It achieves quantitative description of concepts through the concept of clouds.The theoretical principle is that clouds are composed of many cloud droplets, each representing a possible value and its membership degree.This theory introduces probability theory and fuzzy set theory, and its advantage lies in dealing with uncertainty and fuzziness problems, especially in fields such as data mining, intelligent control, and decision support.It provides an effective tool and method for handling uncertain information, improving the accuracy and adaptability of decision-making.

Re-building of the early warning evaluation system for the EMC in higher education institutions
Considering a new competitive environment and the strategic evaluation needs for enterprises' scale development, higher education institutions should highlight that the development of management practices is the condition for modern enterprises to employ new performance evaluation methods.In addition, comprehensive factors should be taken into consideration, including those aspects related to enterprise development goals, organizational structure, organizational capabilities, comprehensive budget management, incentive mechanisms, indicator quantification, internal controls, and financial analysis.Moreover, based on the core BSC ideas of enterprise performance evaluation, scientific methods of fuzzy judgment and analytic hierarchy process (AHP) were adopted to determine each indicator weight in the comprehensive evaluation function model.Finally, beyond operating as a teaching method for stronger enterprise management, this approach also contributes to a new regional/ global digital decision system.
More specifically, the first step is to cultivate relevant abilities and confirm board performance, then the next to optimize indicator selection combining the characteristics of simulation enterprise on the platform and expert opinions, so as to develop a comprehensive evaluation indicator system for early warning based on cause-and-effect relationship chain and strategic mapping.To be noted, the target, or the enterprise's strategic goals, was initially divided by AHP into four layers from top to bottom.In detail, the first layer represents the comprehensive early warning evaluation layer (the comprehensive layer), showing the overall dashboard performance.Then this layer was further sub-divided into five specific strategic decision layers (the first-level dimensions), namely the dimensions of business, finance, and operation, customer management and marketing, internal operational objectives, product development strategies, along with team building and management.Furthermore, each strategic decision layer was broken down into tactical operational layers (the second-level dimensions), which further divided into specific management execution indicators (the third-level dimensions).Particularly, the relevance of each indicator to the indicator system was identified, finally shaping a four-layer comprehensive evaluation indicator system featured by a "one overall and three-dimensional" framework (as shown in Table 1).
This study adopts a three-level indicator system to identify the positive and negative correlation of specific indicators at the third level.(1) Positive correlation indicates that the larger the indicator value is; the higher utility the indicator system is.( 2  (1) Overall product benefits, which reflects the contribution of product value.(2) Product structure, which exhibits the product mix from the perspective of quantity and value.
Specific indicators for team building and management include: (1) Team overall satisfaction, which values employees' satisfaction on the company's working environment.(2) Team organizational level, which measures the effectiveness of organizational management within the company.(3) The ability of being a learning organization, which reflects the progress of human capital.

Subjective weights determined by AHP.
The Analytic Hierarchy Process (AHP) is a comprehensive analysis method integrating qualitative and quantitative analysis.Proposed by Professor A.L. Saaty of the University of Pittsburgh in the 1970s, this method applies network system theory to solve multi-objective comprehensive evaluation problems.By assigning weights to decision-related indicators in a hierarchical order, it provides a clear and straightforward solution to the ranking of multi-objective, multi-criteria, and multi-level factors.Specific implementation process is described in Fig 3. Firstly, a judgment matrix A is constructed as (a ij ) m×m for each hierarchical level corresponding to the three dimensions in Table 1.Then, technical experts and scholars are invited to compare the importance of each pair of indicators within the hierarchical level with a scale of 1 to 9 to determine the judgment matrices for each level.

First-level dimensions Second-level dimensions Third-level indicator dimensions and correlation
Team building and management dimension U5

Normalizing the judgment matrices at each level and obtaining the normalized vector w i of the i-th indicator. w i ¼
ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi

Calculating the maximum eigenvalue λ max of judgment matrix with MMULT function and checking the consistency of CR.
Calculating the consistency index CI When CI = 0, it shows that there is consistency in the judgment matrix, and the larger the value is, the worse the consistency is.
By looking up the table, we can get the average random consistency index RI.
When CR�0.1, the judgment matrix has an acceptable degree of consistency as needed, otherwise it should be adjusted.

Determining the objective weight based on entropy weight method.
The entropy weight method, put forward by Shannon in the 1940s, is used to determine the weight based on the dispersion of objective data.Weights are assigned to indicators according to the information entropy formula, and then adjusted to ensure objectivity.

Normalizing the third-level indicator matrix for each second-level indicator.
The second-level indicators are classified into positive and negative ones according to the attributes of their relevant third-level indicators as shown in Table 1.For positive indicators, the higher score the comprehensive evaluation result is, the more the indicator contributes to the one at a higher level.For negative indicators, the higher score the comprehensive evaluation result is, the less the contribution it makes.As the third-level indicators under the second-level ones have different measurement, it is necessary to standardize them to ensure comparability.The specific approach is as follows: first, the absolute values of indicators are converted into relative values to avoid the influence of dimensional difference on evaluation results.By doing so, normalized values would fall within the range of [0, 1].The indicator system should be normalized using the min-max method.The positive indicator is applied with Eq (6) and negative one, Eq (7).The formulas are as follows: Where, x j = the value of the j-th indicator; x max = the maximum value among different evaluation series of the same indicator; x min = the minimum value among different evaluation series of the same indicator.

Data shift.
In case meaningless data such as zero and negative values affect the calculation of subsequent data, this study adopts a data shift strategy to resolve this problem, by adding a correction value α to each normalized datum.

4.2.2.4
Calculating the j-th information entropy value e j for each indicator.
Where, K is a constant determined by the number of samples.

4.2.2.5
Calculating the j-th information utility value d j for each indicator.
The information utility value for a particular indicator, denoted as x ij , depends on the difference between 1 and the information entropy value e j of that indicator.This information utility value directly influences the weight assigned to the indicator x ij .A higher information utility value indicates greater importance of the indicator and such indicator is assigned with a high weight.

Determining the weight mix for the third-level indicators under the corresponding second-level indicators.
The weight mix Wi for the third-level indicators under the corresponding second-level indicators can be calculated as: subjective weights determined by the AHP multiply the proportion of the weight mix α, and objective weights determined by the Entropy Weight Method multiply the proportion (1-α).The process is as follows: Where, Wi = the weight mix; ω i = the subjective weight; ω ij = the objective weight; α = 0.65.

Designing the comprehensive early warning evaluation system based on the cloud model
The model of the comprehensive early warning evaluation system of the college simulation enterprise management cockpit is designed based on the principle of cloud modeling.It aims at diagnosing qualitative and quantitative uncertainty transformation of the talent cultivation capacity system in large-scale interdisciplinary simulation platforms.

Constructing the authoritative evaluation set.
As common practice shows, three evaluation levels are confirmed according to conventional rating standards, and marked in different colors based on the GE matrix theory.The three levels are: the warning level marked in green, the alert level in yellow, and the danger level in red.A dataset of companies' three-level indicators is obtained.These companies feature good performance over the years in selected industries.

Transforming the indicator sample dataset into the score one.
Based on practical experience, knowledge, and individual opinions about enterprise management of experts on and off campus, statistical evaluation is applied to each third-level indicator of the selected company.By analyzing the aggregation of scatterplot sample group data, the corresponding warning range [x jmin , x jmax ] for the sample group is determined, which serves as the warning zone on the cockpit dashboard.Then, considering the positive or negative correlation of the indicators, a score pre-warning interval of [60, 70] is set for positively correlated indicators, and one of [70, 60] set for negatively ones.Moreover, based on two known endpoints (for example, when x j is a positively correlated indicator, the coordinates of the two endpoints determining the pre-warning zone for the indicator score are [x jmin , 60] and [x jmax , 70]), the scoring function for each x j indicator (corresponding to the third-level dimensional indicator U mnt ) is calculated as: Where, a represents the slope of the line.a>0 when the correlation of the indicator is positive and a<0 when the correlation is negative.b is a constant.The value of y j is then adjusted using an if function: if y j <0, it is set to 0, and if y j >100, it is set to 100.Next, the corresponding sample group data for each x j indicator is inputted into the y j function, and the score dataset is obtained.This process is repeated for the entire sample dataset to get the corresponding scores.

Calculating the standard evaluation cloud.
The effective domain H of the evaluation values for each indicator in the score dataset is divided into Z sub-intervals.The Z j -th sub-interval, denoted as (min, max], corresponds to the digital characteristic value of the standard cloud (Ex, En, He).The calculation expressions are as follows: Where, Z max and Z min represent the maximum and minimum value obtained from expert ratings.

4.3.4
Calculating the evaluation cloud for the j-th indicator in the sample score dataset.The evaluation cloud for the j-th indicator in the sample score dataset, denoted as Z j (E xj , Enj, Hej), where j = 1, 2,. .., n, can be calculated as follows: Where, S 2 = the sample variance; n = the number of samples; x i = the individual values in the sample; � X = the sample mean.4.3.5 Calculating the comprehensive evaluation cloud.To calculate the three numerical characteristics of expected value (Ex), entropy (En) and hyper-entropy (He), the following formulas are used: En ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi Once the comprehensive evaluation cloud is calculated, a comparison is made between the first-level evaluation standard cloud diagram obtained by programming in MATLAB and the generated one.By using the same computational principle in the running of the backend model of the management cockpit, the warning method featuring "observing the corresponding cloud droplet values and membership degrees between the observed indicator cloud diagram and the comprehensive evaluation cloud diagram within the most similar warning interval" is simulated on the dashboard.This can directly show the corresponding warning level of operational status where a simulated company is in.

Calculating the weight of evaluation model indicators
This paper studies the case of an integrated Guangzhou Huashang College Internship Simulation Platform for business operation of companies.In each period, a total of 54 manufacturing enterprises and 36 distributors provide internship opportunities for students.As the business operation is cycled, the competition is fierce, and companies cannot assess its operational status in a timely manner, it is urgent to put in place a visual tool to track the real-time operation process of the companies, which can provide diagnostic warnings against business risks.Key risks include cash budgeting, bankruptcy, capital structure, operational efficiency, product development, customer management, team building, learning ability and growth.To address the issue in a scientific-based approach, this study adopts a weight mix method to score relevant indicators.A total of 12 experts from the college and 3 experts from Kingdee Precision Information Technology Service Co., Ltd. are invited to score the subjective weighting.Aggregation intervals for scatter plot warnings referenced by objective indicator scoring are selected.Furthermore, the calculation results are adjusted to ensure their validity.It is worth noting that the following indicators correspond to column U in Table 1.

Calculating the weight of the evaluation model indicators using AHP and entropy weight method
The first-level indicators are paired up to evaluate their relative importance by experts and the importance of the indicator weight is scored.The second-level and third-level indicators should do the same.Subjective weights for indicators of three levels are calculated according to Eqs (1) to (5).Next, based on the data of 20 manufacturing enterprises that have performed   "General" and "Good", the comprehensive cloud map is further analyzed to study its numerical features.It is determined that the cloud map is most similar to the "Good" standard one.Therefore, the safety evaluation level for enterprises on the Guangzhou Huashang College Simulation Platform is determined to be "Good".This indicates that these enterprises can operate in a good state; however, continuous improvement is needed in key areas such as the main business profit margin U145, operating leverage ratio U317, current ratio U333, quick ratio U334, cash ratio U339, and cash flow ratio U3310, in case of a shift to the dangerous level.

Main conclusions
Firstly, the enterprise management cockpit is a tool that utilizes data visualization and information technology to present key performance indicators of an organization.It allows the management personnel to access key data on an integrated platform, so as to better understand the condition and development of the enterprise.Researches have shown that this management approach is of great significance in improving the overall operational management level of enterprises on the college simulation platform.It helps the management personnel to gain a better understanding of the business through macro system and micro indicators and find risks in advance, and supports their decision-making.
Secondly, an evaluation indicator system for the operational safety of enterprises is established based on five management dimensions: integration of business and finance, customers, internal operations, products, and teams.This system reflects various factors necessary to assess the safety of enterprises on the simulation platform.By using the subjective weights derived from the AHP and the objective weights calculated through the entropy weight method, and the weight mix of each indicator is obtained in proportion.This approach reflects the importance of each indicator in an objective way and provides reliable data for the comprehensive warning evaluation of the college simulation enterprise management cockpit cloud model.It bridges the gap in the digitalization of the platform throughout the warning diagnosis process.
Thirdly, the application of cloud models in establishing a safety evaluation model for simulation platform enterprises enables a comprehensive understanding of the overall operational safety.It allows real-time feedback on the warning levels under different operational conditions of enterprises.

Fig 1 .Fig 2 .
Fig 1. Guangzhou Huashan College's simulation intern enterprise management cockpit.https://doi.org/10.1371/journal.pone.0305652.g001 ) Negative correlation represents the opposite.Specific indicators for business and finance include: (1) Financial warning, which measures operational risks from the financial perspective.(2) Budget implementation deviation, which measures the level of financial control in terms of budget effectiveness.(3) Business valueadded efficiency, which reflects the value of investment to shareholders.(4) Operational incentive quality, which measures the operational efficiency from the financial perspective.(5) Level of total scale, which reflects the operational status in quantitative ways.Specific indicators for customer management and marketing include: (1) Proportion of attention customers, which analyzes the contribution of different types of customers.(2) Key customer segmentation, which explains the effectiveness of management on important customers.(3) Customer satisfaction, which reflects to what extent products or services satisfy consumers.Specific indicators for internal operational objectives include: (1) Business warning, which measures operational risks from a business perspective.(2) Operational support, which reflect

Fig 4 .
Fig 4.An example of the warning area division on the management dashboard of internship simulation platform for enterprise operation at Guangzhou Huashang College.https://doi.org/10.1371/journal.pone.0305652.g004

Table 1 ,
the comprehensive early warning evaluation indicator system proposed by this study is structured as follows.The top-level is the objective layer, denoted as U.The first-level indicators are known as U 1 , U 2 , . .., U m .The second-level indicators are recorded as U 11 , U 12 , . .., U mn .The third-level indicators are denoted as U 111 , U 112 , . .., U mnt .4.2.1.2Constructing comparative judgment matrices.